A Textual Case-Based Reasoning Framework for Knowledge Management Applications
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Transcript of A Textual Case-Based Reasoning Framework for Knowledge Management Applications
A Textual Case-Based Reasoning Framework for Knowledge Management
Applications
German Workshop on CBR March 15, 2001
Rosina WeberDavid W. Aha, Nabil Sandhu, Héctor Muñoz-
Avila Decision Aids Group
Navy Center for Applied Research in Artificial IntelligenceNaval Research Laboratory
University of Wyoming
Outline Introduction
Knowledge Management Systems Knowledge artifacts Lessons Learned Systems
Motivation Methodology
NEO domain Case Representation Elicitation Tool Extraction Tool Monitored Distribution Domain Ontology
Problems vs. Solutions Next Steps
Introduction Knowledge
Management Systems
Knowledge artifacts
Lessons/ Learned Systems
MotivationMethodology
NEO domain Case
Representation Elicitation Tool Extraction Tool Monitored
Distribution Domain
OntologyProblems vs. Solutions
Next Steps
3 R.Weber, NCARAI-NRL, U. of Wyoming
Knowledge Management Systems
KMS manipulate knowledge to...
….storing, distribute, collect, validate, apply, create, sharing & leveraging knowledge
CORPORATE MEMORY
DOCUMENTSKNOWLEDGE ARTIFACTS
Introduction Knowledge
Management Systems
Knowledge artifacts
Lessons/ Learned Systems
MotivationMethodology
NEO domain Case
Representation Elicitation Tool Extraction Tool Monitored
Distribution Domain
OntologyProblems vs. Solutions
Next Steps
4 R.Weber, NCARAI-NRL, U. of Wyoming
Knowledge artifacts are structured formalisms that imply essential elements of knowledge for reuse (e.g., when to reuse, what to reuse) well understood and accepted lessons learned alerts best practices incident reports
Alert systemsLessons Learned systems: our current focus
Introduction Knowledge
Management Systems
Knowledge artifacts
Lessons/ Learned Systems
MotivationMethodology
NEO domain Case
Representation Elicitation Tool Extraction Tool Monitored
Distribution Domain
OntologyProblems vs. Solutions
Next Steps
5 R.Weber, NCARAI-NRL, U. of Wyoming
why? what was the originating event success/failure/advice cause
when to reuse? task/contextual info about the process main index guiding distribution
Lessons refer to one
task/activity/decision of a process
originate from successes, failures, or advice
teach something about a work practice that has the potential to generate a positive impact in the targeted process when reused what to reuse?
what to repeat or avoid
under which conditions?
what is required for the lesson to be applicable?
reuse components
indexing
solution
Weber et al., 2001Intelligent lessons learned systems. International Journal of
Expert Systems Research & Applications, Vol. 20, No. 1, Jan 2001, 17-34.
Introduction Knowledge
Management Systems
Knowledge artifacts
Lessons/ Learned Systems
MotivationMethodology
NEO domain Case
Representation Elicitation Tool Extraction Tool Monitored
Distribution Domain
OntologyProblems vs. Solutions
Next Steps
6 R.Weber, NCARAI-NRL, U. of Wyoming
Motivation (i) LLS are not used
lessons are distributed outside the context of reuse
lessons are collected in textual descriptions, so they are:poorly representeddifficult to be retrieved& difficult to be interpreted
Introduction Knowledge
Management Systems
Knowledge artifacts
Lessons/ Learned Systems
MotivationMethodology
NEO domain Case
Representation Elicitation Tool Extraction Tool Monitored
Distribution Domain
OntologyProblems vs. Solutions
Next Steps
7 R.Weber, NCARAI-NRL, U. of Wyoming
Motivation (ii)
in terms of reuse elements
artifacts disseminated in the context of external
distribution systems (DS)
reusing knowledge artifacts
share knowledge
Knowledge artifacts as cases
text extraction toolelicitation tool
Domain Ontology + Subset of NLChallenge:Natural language
human users text
Introduction Knowledge
Management Systems
Knowledge artifacts
Lessons/ Learned Systems
MotivationMethodology
NEO domain Case
Representation Elicitation Tool Extraction Tool Monitored
Distribution Domain
OntologyProblems vs. Solutions
Next Steps
8 R.Weber, NCARAI-NRL, U. of Wyoming
TCBR Methodology for Knowledge Management Systems
that manipulate knowledge artifacts
Elicitation Tool Extraction tool Case Representation Monitored Distribution Domain Ontology
case base
extraction tool
human users
textualdocuments
format of external distribution
system
artifacts in the format of ext distribution system
domain specific ontology
elicitation tool
Why CBR?
Why textual?
.
Introduction Knowledge
Management Systems
Knowledge artifacts
Lessons/ Learned Systems
MotivationMethodology
NEO domain Case
Representation Elicitation Tool Extraction Tool Monitored
Distribution Domain
OntologyProblems vs. Solutions
Next Steps
9 R.Weber, NCARAI-NRL, U. of Wyoming
Noncombatant Evacuation Operations-NEO: military operations to evacuate noncombatants whose lives are in danger to a safe haven
AssemblyPoint
HQ
ISB
safe haven
Noncombatant Evacuation Operations:
military operations to evacuate noncombatants whose lives are in danger to a safe haven
Introduction Knowledge
Management Systems
Knowledge artifacts
Lessons/ Learned Systems
MotivationMethodology
NEO domain Case
Representation Elicitation Tool Extraction Tool Monitored
Distribution Domain
OntologyProblems vs. Solutions
Next Steps
10 R.Weber, NCARAI-NRL, U. of Wyoming
Noncombatant Evacuation Operations (NEO)
Assembly Point
NEO site safe haven
ISB
HQ
Introduction Knowledge
Management Systems
Knowledge artifacts
Lessons/ Learned Systems
MotivationMethodology
NEO domain Case
Representation Elicitation Tool Extraction Tool Monitored
Distribution Domain
OntologyProblems vs. Solutions
Next Steps
11 R.Weber, NCARAI-NRL, U. of Wyoming
Case Representation Elicitation Tool Extraction tool Monitored Distribution Domain Ontology
TCBR Methodology for Knowledge Management Systems
that manipulate knowledge artifacts
Introduction Knowledge
Management Systems
Knowledge artifacts
Lessons/ Learned Systems
MotivationMethodology
NEO domain Case
Representation Elicitation Tool Extraction Tool Monitored
Distribution Domain
OntologyProblems vs. Solutions
Next Steps
12 R.Weber, NCARAI-NRL, U. of Wyoming
Case Representation
Example:1. When/Where to reuse (which task): Registering evacuees
Context/Process: NEO operation2. Under which conditions: The weather is hot and humid. The location is a tropical country.3. What to reuse: Make sure to avoid registration in 3 steps.4. Why (originating event): We implemented registration in 3 steps.
Success/Failure: It was a failure.Why? It was very time consuming.
It caused evacuee discomfort.Additional elements provided by the domain ontology.
Introduction Knowledge
Management Systems
Knowledge artifacts
Lessons/ Learned Systems
MotivationMethodology
NEO domain Case
Representation Elicitation Tool Extraction Tool Monitored
Distribution Domain
OntologyProblems vs. Solutions
Next Steps
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Requirements: Indentify the audience style Identify reuse & retrieve
components: knowledge, process, conditions of applicability, explanation
Identify the format of components Identify relationships
Case Representation
Introduction Knowledge
Management Systems
Knowledge artifacts
Lessons/ Learned Systems
MotivationMethodology
NEO domain Case
Representation Elicitation Tool Extraction Tool Monitored
Distribution Domain
OntologyProblems vs. Solutions
Next Steps
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Elicitation ToolWhat: The lesson elicitation tool LET guides and
educates users to submit lessons in the CR It orients with examples and reduces the
amount of text to type by giving drop-down lists to select from
It requests confirmations to orient the user to rethink the experience to be communicated
A domain ontology supports disambiguation at run-time (do not store unless relevant)
Uses a subset of NL based on the CRF by using a template-based elicitation with pre-defined grammar structures to overcome NLP problems
Introduction Knowledge
Management Systems
Knowledge artifacts
Lessons/ Learned Systems
MotivationMethodology
NEO domain Case
Representation Elicitation Tool Extraction Tool Monitored
Distribution Domain
OntologyProblems vs. Solutions
Next Steps
15 R.Weber, NCARAI-NRL, U. of Wyoming
Elicitation Tool
Requirements: in connectivity with the domain
ontology be supported by lexicons of
expressions, domain entities and verbs
support conversation to acquire new concepts for the ontology
Example: Microsoft PowerPoint
Presentation
Introduction Knowledge
Management Systems
Knowledge artifacts
Lessons/ Learned Systems
MotivationMethodology
NEO domain Case
Representation Elicitation Tool Extraction Tool Monitored
Distribution Domain
OntologyProblems vs. Solutions
Next Steps
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What: converts texts into knowledge
artifacts template mining
variant of Information Extraction search for specific descriptions in
selected excerpts of text (structure)
avoids NLP techniques uses methods that contain
knowledge of where to search and what to extract
Extraction Tool
Introduction Knowledge
Management Systems
Knowledge artifacts
Lessons/ Learned Systems
MotivationMethodology
NEO domain Case
Representation Elicitation Tool Extraction Tool Monitored
Distribution Domain
OntologyProblems vs. Solutions
Next Steps
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Requirements:
Source text must follow stereotypical style
Source text must have some structure that allows identification of a rhetorical structure
Domain of source text is known
Extraction Tool
Introduction Knowledge
Management Systems
Knowledge artifacts
Lessons/ Learned Systems
MotivationMethodology
NEO domain Case
Representation Elicitation Tool Extraction Tool Monitored
Distribution Domain
OntologyProblems vs. Solutions
Next Steps
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Example: Method converting textual lessons into the case representation framework:
“In field recommended action, search for expressions such as (in this order): make sure , ensure, should. When (if) one of these is found, extract content beginning right after the expression found until the next period.”
Extraction Tool
Introduction Knowledge
Management Systems
Knowledge artifacts
Lessons/ Learned Systems
MotivationMethodology
NEO domain Case
Representation Elicitation Tool Extraction Tool Monitored
Distribution Domain
OntologyProblems vs. Solutions
Next Steps
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Monitored DistributionWhat: a framework to solve the lesson
distribution gap disseminate knowledge in the context
of targeted processes (just in time knowledge)
infrequent variable experiential knowledge
allows executable implementation of knowledgeExample: Microsoft PowerPoint
Presentation
Introduction Knowledge
Management Systems
Knowledge artifacts
Lessons/ Learned Systems
MotivationMethodology
NEO domain Case
Representation Elicitation Tool Extraction Tool Monitored
Distribution Domain
OntologyProblems vs. Solutions
Next Steps
20 R.Weber, NCARAI-NRL, U. of Wyoming
Monitored DistributionRequirements: The conversion of the knowledge artifacts into the format of the external distribution systems. Evaluation:We have evaluated the monitored distribution in two domains:
domain/measure
travel duration NEO duration NEO casualties
no lessons
9h45 39h50 11.5
with lessons reduction
32h48 8.7
9h14 5%
18% 24%
Introduction Knowledge
Management Systems
Knowledge artifacts
Lessons/ Learned Systems
MotivationMethodology
NEO domain Case
Representation Elicitation Tool Extraction Tool Monitored
Distribution Domain
OntologyProblems vs. Solutions
Next Steps
21 R.Weber, NCARAI-NRL, U. of Wyoming
Domain OntologyWhat: A hierarchical model of domain
knowledge where concepts are organized according to their commonalities and meaning
It supports the CR, similarity assessment, knowledge elicitation, text extraction, and the conversion of artifacts into the format of external distribution systems
We are currently investigating corpus analysis to learn lexicons, concepts, and relations from about 40,000 lessons
Introduction Knowledge
Management Systems
Knowledge artifacts
Lessons/ Learned Systems
MotivationMethodology
NEO domain Case
Representation Elicitation Tool Extraction Tool Monitored
Distribution Domain
OntologyProblems vs. Solutions
Next Steps
22 R.Weber, NCARAI-NRL, U. of Wyoming
Domain OntologyExample: Condition complement:
“ it is a disaster relief operation.” Operation cause:
“ disaster relief” Operation hostility level:
“permissive” to the “hostility level”. Requirements: Knowledge acquisition from domain experts Automatic acquisition
Microsoft PowerPoint Presentation
Introduction Knowledge
Management Systems
Knowledge artifacts
Lessons/ Learned Systems
MotivationMethodology
NEO domain Case
Representation Elicitation Tool Extraction Tool Monitored
Distribution Domain
OntologyProblems vs. Solutions
Next Steps
23 R.Weber, NCARAI-NRL, U. of Wyoming
Next Steps
learning ontology support conversation to
acquire new concepts for the ontology
evaluating the elicitation tool implementing text extraction
for all reuse components evaluating extraction tool
Fourth International Conference on CBR
30 July – 2 August 2001Vancouver, BC (Canada)Premiere CBR meetingIndustry DayExhibition5 WorkshopsGreat social schedule!
www.iccbr.org/iccbr01Chair: Qiang Yang Program Chairs: David W. Aha, Ian Watson
Workshop ChairsRosina Weber & Cristiane Gresse von Wangenheim
1. Process-Oriented KM Kurt D. Fenstermacher, Carsten Tautz2. Soft Computing Simon C.K. Shiu
3. Authoring Support David Patterson, Agnar Aamodt, Barry Smyth 4. Creative SystemsCarlos Bento, Amilcar Cardoso
5. CBR in E-Commerce Robin Burke